Big data and data science are not just some technical jargons but are significant concepts contributing in the field of technology. While these terms are interlinked there is a huge fundamental difference between them.
What is big data ? Big data refers to the huge volumes of data of various types, i.e., structured, semi-structured, and unstructured. This data is generated through various digital channels like mobile, internet, social media, e-commerce websites. Big data has proven to be of great use since its inception as companies started realizing its importance for various business purposes. Now that the companies have started analyzing this data they have witnessed exponential growth over the years.
Benefits of big data processing?Ability to process Big Data brings in multiple benefits, such as-
- Businesses can utilize outside intelligence while taking decisions
- Access to social data from search engines and sites like facebook, twitter are enabling
- Traditional customer feedback systems are getting replaced by new systems designed with Big Data technologies. In these new systems, Big Data and natural language processing technologies are being used to read and evaluate consumer responses.
- Early identification of risk to the product/services, if any
- Using big data increases your efficiency
- Big Data technologies can be used for creating a staging area or landing zone for new data before identifying what data should be moved to the data warehouse.
What is data science? What are the benefits of data science?
Data science deals with slicing and dicing of the big chunks of data as well as finding insightful patterns and trends using technology, mathematics and statistical techniques. The data scientists are responsible for uncovering the facts hidden in the complex web of unstructured data so as to be used in making business decisions. Data analytics companies perform the introductory job by developing heuristics algorithms and models that can be used in future for significant purposes. This amalgamation of technology and concepts make data science a potential field for lucrative career opportunities.
Key differences between big data and data science?
1) Organizations need big data to improve efficiencies, understand new markets, and enhance competitiveness whereas data science provides the methods or mechanisms to understand and utilize the potential of big data in a timely manner.
2) Currently, for organizations, there is no limit to the amount of valuable data that can be collected, but to use all this data to extract meaningful information for organizational decisions, data science is needed.
3) Big data provides the potential for performance. However, digging out insight information from big data for utilizing its potential for enhancing performance is a significant challenge. Data science uses theoretical and experimental approaches in addition to deductive and inductive reasoning. Takes responsibility to uncover all hidden insightful information from a complex network of unstructured data thus supporting organizations to realize the potential of big data.
4) Big data analysis performs mining of useful information from large volumes of datasets. Contrary to analysis, data science makes use of machine learning algorithms and statistical methods to train the computer to learn without much programming to make predictions from big data. Hence data science must not be confused with big data analytics.
5) Big data employs complex technological tools like parallel computing and other automation tools to handle the “big data”. Data science technologies use predictive and statistical modeling with relatively simple tools.
From the above differences between big data and data science, it may be noted that data science is included in the concept of big data. Data science plays an important role in many application areas and it works on big data to derive useful insights through predictive analysis where results are used to make smart decisions. Therefore, data science is included in big data rather than the other way round.